INTRODUCTIONThe importance of forecast accuracy has been widely recognized by logistics researchers and practitioners since forecasts form an essential input for many operational, tactical, and strategic logistics decisions (Lambert, Stock, and Ellram 1998). Poor forecast accuracy can not only harm a firm's performance, but also negatively impact upstream members of its supply chain in the form of the bullwhip effect (Forslund and Jonsson 2007; Lee, Padmanabhan, and Wang 1997). Forecast accuracy continues to receive attention from academicians and practitioners as evidenced by the existence of such organizations as the Institute of Business Forecasting and International Institute of Forecasters, as well as conferences focused on supply chain planning and forecasting that are offered around the world.One way to improve forecast accuracy is to judgmentally adjust statistical forecasts in order to combine information from multiple sources and to exploit the respective strengths of statistical and judgmental forecasting methods (Armstrong 2001a). Industry surveys indicate that judgmentally adjusting statistical forecasts is a common practice. In a study of U.S. firms, Sanders and Manrodt (1994) found that 45 % of the respondents "always" adjusted statistical forecasts and 37 % did so "sometimes." In a similar study of Canadian firms, Klassen and Flores (2001) reported that 80 % of the respondents that used computer-generated statistical forecasts judgmentally adjusted them.In addition to being a popular forecasting practice, judgmental adjustments are an integral part of demand collaboration initiatives such as Collaborative Planning, Forecasting, and Replenishment (CPFR). The goal of CPFR is to improve coordination of supply chain activities between trading partners by acting on demand plans that combine information from multiple sources (VICSA 2002). This involves regular updating of sales and order forecasts by designated individuals based on information obtained from different trading partners. As such, judgmental adjustments are a regular part of logistics managers' responsibilities when they are involved in demand collaboration initiatives.The purpose of this research is to expand the extant literature in two distinct ways. First, we move beyond a forecasting program level to analyze the effects of two individual differences variables; namely motivational orientation and gender; which have been neglected by previous research on forecasting performance. Motivational orientation refers to an individual's propensity to be motivated by various factors (e.g., financial rewards, social recognition). Second, the unit of analysis is the individual forecaster instead of an observed sample as in previous studies. This approach exposes inter-individual differences in forecasting performance to a greater extent and enables us to capture the heterogeneity in the forecasting performance of individual forecasters. In other words, we will investigate the relationship between a forecaster's motivational orientation and forecas...